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I haven't tested but to count number of trainable parameters for which gradients are activated, we can do something like: num_trainable = sum([param.numel() for param in model.parameters() if param.requires_grad]) You can add this after the weights of vae, text_encoder and unet are frozen (see this). |
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This is just a simple question. (I am new to here 🥲)
I am using
train_dreambooth_lora.py
, how can I count the number of activated parameters?Beta Was this translation helpful? Give feedback.
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